Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations300153
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.5 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical2

Alerts

airline is highly overall correlated with flightHigh correlation
class is highly overall correlated with df_index and 1 other fieldsHigh correlation
df_index is highly overall correlated with class and 1 other fieldsHigh correlation
duration is highly overall correlated with stopsHigh correlation
flight is highly overall correlated with airlineHigh correlation
price is highly overall correlated with class and 1 other fieldsHigh correlation
stops is highly overall correlated with durationHigh correlation
stops is highly imbalanced (50.6%) Imbalance
df_index is uniformly distributed Uniform
df_index has unique values Unique
airline has 16098 (5.4%) zeros Zeros
source_city has 52061 (17.3%) zeros Zeros
departure_time has 47794 (15.9%) zeros Zeros
arrival_time has 38139 (12.7%) zeros Zeros
destination_city has 51068 (17.0%) zeros Zeros

Reproduction

Analysis started2025-08-02 10:02:18.607105
Analysis finished2025-08-02 10:02:39.134888
Duration20.53 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct300153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150076
Minimum0
Maximum300152
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:39.281930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15007.6
Q175038
median150076
Q3225114
95-th percentile285144.4
Maximum300152
Range300152
Interquartile range (IQR)150076

Descriptive statistics

Standard deviation86646.852
Coefficient of variation (CV)0.57735315
Kurtosis-1.2
Mean150076
Median Absolute Deviation (MAD)75038
Skewness0
Sum4.5045762 × 1010
Variance7.507677 × 109
MonotonicityStrictly increasing
2025-08-02T10:02:39.478307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300152 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
Other values (300143) 300143
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
300152 1
< 0.1%
300151 1
< 0.1%
300150 1
< 0.1%
300149 1
< 0.1%
300148 1
< 0.1%
300147 1
< 0.1%
300146 1
< 0.1%
300145 1
< 0.1%
300144 1
< 0.1%
300143 1
< 0.1%

airline
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1048732
Minimum0
Maximum5
Zeros16098
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:39.629548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.8332648
Coefficient of variation (CV)0.59044756
Kurtosis-1.5920362
Mean3.1048732
Median Absolute Deviation (MAD)2
Skewness-0.21131816
Sum931937
Variance3.3608598
MonotonicityNot monotonic
2025-08-02T10:02:39.767817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 127859
42.6%
1 80892
27.0%
3 43120
 
14.4%
2 23173
 
7.7%
0 16098
 
5.4%
4 9011
 
3.0%
ValueCountFrequency (%)
0 16098
 
5.4%
1 80892
27.0%
2 23173
 
7.7%
3 43120
 
14.4%
4 9011
 
3.0%
5 127859
42.6%
ValueCountFrequency (%)
5 127859
42.6%
4 9011
 
3.0%
3 43120
 
14.4%
2 23173
 
7.7%
1 80892
27.0%
0 16098
 
5.4%

flight
Real number (ℝ)

High correlation 

Distinct1561
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1088.3385
Minimum0
Maximum1560
Zeros51
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:39.930310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile258
Q1783
median1142
Q31486
95-th percentile1547
Maximum1560
Range1560
Interquartile range (IQR)703

Descriptive statistics

Standard deviation426.69135
Coefficient of variation (CV)0.39205757
Kurtosis-0.69662678
Mean1088.3385
Median Absolute Deviation (MAD)347
Skewness-0.60046202
Sum3.2666806 × 108
Variance182065.51
MonotonicityNot monotonic
2025-08-02T10:02:40.125904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1442 3235
 
1.1%
1454 2741
 
0.9%
1445 2650
 
0.9%
1490 2542
 
0.8%
1477 2468
 
0.8%
1483 2440
 
0.8%
1518 2423
 
0.8%
1486 2404
 
0.8%
1481 2335
 
0.8%
1508 2329
 
0.8%
Other values (1551) 274586
91.5%
ValueCountFrequency (%)
0 51
< 0.1%
1 39
< 0.1%
2 5
 
< 0.1%
3 49
< 0.1%
4 20
 
< 0.1%
5 5
 
< 0.1%
6 94
< 0.1%
7 51
< 0.1%
8 91
< 0.1%
9 48
< 0.1%
ValueCountFrequency (%)
1560 1266
0.4%
1559 1024
0.3%
1558 1273
0.4%
1557 911
0.3%
1556 1381
0.5%
1555 1012
0.3%
1554 1002
0.3%
1553 996
0.3%
1552 924
0.3%
1551 782
0.3%

source_city
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5775921
Minimum0
Maximum5
Zeros52061
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:40.297918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7517617
Coefficient of variation (CV)0.67961169
Kurtosis-1.2902317
Mean2.5775921
Median Absolute Deviation (MAD)2
Skewness-0.033005957
Sum773672
Variance3.0686691
MonotonicityNot monotonic
2025-08-02T10:02:40.437611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 61343
20.4%
5 60896
20.3%
0 52061
17.3%
4 46347
15.4%
3 40806
13.6%
1 38700
12.9%
ValueCountFrequency (%)
0 52061
17.3%
1 38700
12.9%
2 61343
20.4%
3 40806
13.6%
4 46347
15.4%
5 60896
20.3%
ValueCountFrequency (%)
5 60896
20.3%
4 46347
15.4%
3 40806
13.6%
2 61343
20.4%
1 38700
12.9%
0 52061
17.3%

departure_time
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4173372
Minimum0
Maximum5
Zeros47794
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:40.569270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7542762
Coefficient of variation (CV)0.72570604
Kurtosis-1.4218339
Mean2.4173372
Median Absolute Deviation (MAD)2
Skewness0.14775021
Sum725571
Variance3.0774849
MonotonicityNot monotonic
2025-08-02T10:02:40.709288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 71146
23.7%
1 66790
22.3%
2 65102
21.7%
5 48015
16.0%
0 47794
15.9%
3 1306
 
0.4%
ValueCountFrequency (%)
0 47794
15.9%
1 66790
22.3%
2 65102
21.7%
3 1306
 
0.4%
4 71146
23.7%
5 48015
16.0%
ValueCountFrequency (%)
5 48015
16.0%
4 71146
23.7%
3 1306
 
0.4%
2 65102
21.7%
1 66790
22.3%
0 47794
15.9%

stops
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
250863 
2
36004 
1
 
13286

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300153
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 250863
83.6%
2 36004
 
12.0%
1 13286
 
4.4%

Length

2025-08-02T10:02:40.865479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-02T10:02:41.000573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 250863
83.6%
2 36004
 
12.0%
1 13286
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 250863
83.6%
2 36004
 
12.0%
1 13286
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 250863
83.6%
2 36004
 
12.0%
1 13286
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 250863
83.6%
2 36004
 
12.0%
1 13286
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 250863
83.6%
2 36004
 
12.0%
1 13286
 
4.4%

arrival_time
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0740855
Minimum0
Maximum5
Zeros38139
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:41.126675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7416663
Coefficient of variation (CV)0.56656404
Kurtosis-1.1530744
Mean3.0740855
Median Absolute Deviation (MAD)1
Skewness-0.40287425
Sum922696
Variance3.0334016
MonotonicityNot monotonic
2025-08-02T10:02:41.263613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 91538
30.5%
2 78323
26.1%
4 62735
20.9%
0 38139
12.7%
1 15417
 
5.1%
3 14001
 
4.7%
ValueCountFrequency (%)
0 38139
12.7%
1 15417
 
5.1%
2 78323
26.1%
3 14001
 
4.7%
4 62735
20.9%
5 91538
30.5%
ValueCountFrequency (%)
5 91538
30.5%
4 62735
20.9%
3 14001
 
4.7%
2 78323
26.1%
1 15417
 
5.1%
0 38139
12.7%

destination_city
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5883033
Minimum0
Maximum5
Zeros51068
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:41.402082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7445431
Coefficient of variation (CV)0.67401032
Kurtosis-1.2904096
Mean2.5883033
Median Absolute Deviation (MAD)2
Skewness-0.054994808
Sum776887
Variance3.0434307
MonotonicityNot monotonic
2025-08-02T10:02:41.545962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 59097
19.7%
2 57360
19.1%
0 51068
17.0%
4 49534
16.5%
3 42726
14.2%
1 40368
13.4%
ValueCountFrequency (%)
0 51068
17.0%
1 40368
13.4%
2 57360
19.1%
3 42726
14.2%
4 49534
16.5%
5 59097
19.7%
ValueCountFrequency (%)
5 59097
19.7%
4 49534
16.5%
3 42726
14.2%
2 57360
19.1%
1 40368
13.4%
0 51068
17.0%

class
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
1
206666 
0
93487 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300153
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 206666
68.9%
0 93487
31.1%

Length

2025-08-02T10:02:41.700557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-02T10:02:41.828478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 206666
68.9%
0 93487
31.1%

Most occurring characters

ValueCountFrequency (%)
1 206666
68.9%
0 93487
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 206666
68.9%
0 93487
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 206666
68.9%
0 93487
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 206666
68.9%
0 93487
31.1%

duration
Real number (ℝ)

High correlation 

Distinct289
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.138864
Minimum2.17
Maximum25.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:42.251585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.17
5-th percentile2.17
Q16.83
median11.25
Q316.17
95-th percentile25.92
Maximum25.92
Range23.75
Interquartile range (IQR)9.34

Descriptive statistics

Standard deviation6.9188797
Coefficient of variation (CV)0.56997752
Kurtosis-0.68168961
Mean12.138864
Median Absolute Deviation (MAD)4.67
Skewness0.4788326
Sum3643516.5
Variance47.870896
MonotonicityNot monotonic
2025-08-02T10:02:42.438958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.17 17393
 
5.8%
25.92 15206
 
5.1%
2.25 4036
 
1.3%
2.75 2879
 
1.0%
2.83 2323
 
0.8%
12 2224
 
0.7%
2.33 2053
 
0.7%
7.58 2045
 
0.7%
8 2040
 
0.7%
11.17 1999
 
0.7%
Other values (279) 247955
82.6%
ValueCountFrequency (%)
2.17 17393
5.8%
2.25 4036
 
1.3%
2.33 2053
 
0.7%
2.42 1252
 
0.4%
2.5 1418
 
0.5%
2.58 1166
 
0.4%
2.67 1564
 
0.5%
2.75 2879
 
1.0%
2.83 2323
 
0.8%
2.92 1430
 
0.5%
ValueCountFrequency (%)
25.92 15206
5.1%
25.83 491
 
0.2%
25.75 553
 
0.2%
25.67 653
 
0.2%
25.58 459
 
0.2%
25.5 694
 
0.2%
25.42 515
 
0.2%
25.33 532
 
0.2%
25.25 499
 
0.2%
25.17 609
 
0.2%

days_left
Real number (ℝ)

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.004751
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:42.624072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median26
Q338
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.561004
Coefficient of variation (CV)0.52148178
Kurtosis-1.1562147
Mean26.004751
Median Absolute Deviation (MAD)12
Skewness-0.03546435
Sum7805404
Variance183.90082
MonotonicityNot monotonic
2025-08-02T10:02:42.812394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
25 6633
 
2.2%
18 6602
 
2.2%
39 6593
 
2.2%
32 6585
 
2.2%
26 6573
 
2.2%
24 6542
 
2.2%
19 6537
 
2.2%
31 6534
 
2.2%
33 6532
 
2.2%
40 6531
 
2.2%
Other values (39) 234491
78.1%
ValueCountFrequency (%)
1 1927
 
0.6%
2 4026
1.3%
3 4248
1.4%
4 5077
1.7%
5 5392
1.8%
6 5740
1.9%
7 5703
1.9%
8 5767
1.9%
9 5665
1.9%
10 5822
1.9%
ValueCountFrequency (%)
49 6154
2.1%
48 6078
2.0%
47 6069
2.0%
46 6160
2.1%
45 6314
2.1%
44 6436
2.1%
43 6472
2.2%
42 6497
2.2%
41 6525
2.2%
40 6531
2.2%

price
Real number (ℝ)

High correlation 

Distinct10846
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20493.977
Minimum2436
Maximum63277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-08-02T10:02:42.997600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2436
5-th percentile2436
Q14783
median7425
Q342521
95-th percentile63277
Maximum63277
Range60841
Interquartile range (IQR)37738

Descriptive statistics

Standard deviation21746.461
Coefficient of variation (CV)1.0611148
Kurtosis-0.83281256
Mean20493.977
Median Absolute Deviation (MAD)3929
Skewness0.95491653
Sum6.1513286 × 109
Variance4.7290858 × 108
MonotonicityNot monotonic
2025-08-02T10:02:43.182266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2436 15066
 
5.0%
63277 15017
 
5.0%
54608 1445
 
0.5%
54684 1390
 
0.5%
60978 1383
 
0.5%
60508 1230
 
0.4%
49725 1205
 
0.4%
51707 1205
 
0.4%
5949 1196
 
0.4%
49613 1150
 
0.4%
Other values (10836) 259866
86.6%
ValueCountFrequency (%)
2436 15066
5.0%
2437 21
 
< 0.1%
2438 37
 
< 0.1%
2447 18
 
< 0.1%
2449 11
 
< 0.1%
2456 53
 
< 0.1%
2463 30
 
< 0.1%
2464 307
 
0.1%
2465 50
 
< 0.1%
2468 6
 
< 0.1%
ValueCountFrequency (%)
63277 15017
5.0%
63269 4
 
< 0.1%
63226 15
 
< 0.1%
63218 24
 
< 0.1%
63163 49
 
< 0.1%
63151 1
 
< 0.1%
63121 2
 
< 0.1%
63065 2
 
< 0.1%
63053 19
 
< 0.1%
63027 4
 
< 0.1%

Interactions

2025-08-02T10:02:36.588389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:22.524686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.172845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.636540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.216686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:28.851670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.369530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:31.894711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:33.389249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:35.089698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:36.740575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:22.678469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.323592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.796868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.371549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.007423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.523059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.048431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:33.539320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:35.238224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:36.891414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:22.826478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.465424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.970464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.514635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.157213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.685267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.196208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:33.693517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:35.382112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.058321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:22.989302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.620624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:26.130700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.669101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.322394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.849321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.362680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:33.853127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:35.541483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.209449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:23.135177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.765110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:26.282243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.815295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.463136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.998784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.514497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:34.000752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:35.690486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.362763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:23.282327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.910493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:26.437730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.956689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.608590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:31.143655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.654793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:34.146814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:35.849660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.511418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:23.429210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.056852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:26.587311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:28.097579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.757391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:31.294237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.797431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:34.292373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:36.008571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.661813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:23.575264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.200036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:26.746042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:28.242600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:29.905078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:31.450879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:32.948075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:34.436011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:36.153445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.807988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:23.722462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.343601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:26.902349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:28.387128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.047372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:31.596961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:33.089466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:34.580222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:36.289580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:37.955337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:24.016943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:25.486033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:27.057636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:28.529490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:30.206242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:31.745100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:33.235431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:34.944576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-08-02T10:02:36.434905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-08-02T10:02:43.317585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
airlinearrival_timeclassdays_leftdeparture_timedestination_citydf_indexdurationflightpricesource_citystops
airline1.0000.0360.449-0.0090.054-0.0300.1860.0150.7030.290-0.0270.174
arrival_time0.0361.0000.106-0.004-0.057-0.0290.0270.0190.0460.0400.0440.066
class0.4490.1061.0000.0200.0700.0280.9760.1860.4250.9840.0280.132
days_left-0.009-0.0040.0201.000-0.002-0.0050.014-0.033-0.001-0.266-0.0040.016
departure_time0.054-0.0570.070-0.0021.0000.0010.0840.1180.0760.055-0.0090.077
destination_city-0.030-0.0290.028-0.0050.0011.0000.021-0.003-0.0620.012-0.2230.102
df_index0.1860.0270.9760.0140.0840.0211.0000.1820.1780.658-0.0920.118
duration0.0150.0190.186-0.0330.118-0.0030.1821.0000.1950.3180.0070.665
flight0.7030.0460.425-0.0010.076-0.0620.1780.1951.0000.3190.0260.168
price0.2900.0400.984-0.2660.0550.0120.6580.3180.3191.0000.0140.280
source_city-0.0270.0440.028-0.004-0.009-0.223-0.0920.0070.0260.0141.0000.063
stops0.1740.0660.1320.0160.0770.1020.1180.6650.1680.2800.0631.000

Missing values

2025-08-02T10:02:38.142091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-02T10:02:38.597050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

df_indexairlineflightsource_citydeparture_timestopsarrival_timedestination_cityclassdurationdays_leftprice
00414082225512.1715953
11413872124512.3315953
22012132121512.1715956
33515592420512.2515955
44515492424512.3315955
55515412420512.3315955
66515332424512.1716060
77515432022512.1716060
88210132124512.1715954
99210142022512.2515954
df_indexairlineflightsource_citydeparture_timestopsarrival_timedestination_cityclassdurationdays_leftprice
300143300143172211053017.424951345
300144300144176112043018.924951345
300145300145171614043023.084951345
300146300146172211043025.924951345
300147300147177611053017.254963277
3001483001485147714023010.084963277
3001493001495148110053010.424963277
3001503001505148611053013.834963277
3001513001515148311023010.004963277
3001523001525147714023010.084963277